Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm

Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm

In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate ment...

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Bibliographic Details
Main Authors: Ala Hag, Dini Handayani, Maryam Altalhi, Thulasyammal Pillai, Teddy Mantoro, Mun Hou Kit, Fares Al-Shargie
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Sensors
Subjects:
brain–computer interface (BCI)
electroencephalography (EEG)
stress state recognition
feature selection
particle swarm optimization (PSO)
mRMR
Online Access:https://www.mdpi.com/1424-8220/21/24/8370

Internet

https://www.mdpi.com/1424-8220/21/24/8370

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